MegaTrans – human transporter machine learning models

MegaTrans — 人类运输机机器学习模型

• 批准号: 10546264
• 负责人: Nathan J Cherrington
• 金额: $ 86.48万
• 依托单位: COLLABORATIONS PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546264
• 关键词: 3-Dimensional; Agrochemicals; Algorithms; Angiotensin-Converting Enzyme Inhibitors; Antiviral Agents; Arizona; Bayesian Method; Bayesian learning; Behavior; Biological Assay; Blood-Testis Barrier; COVID-19 treatment; CRISPR/Cas technology; Chemistry; Client; Clinical; Code; Collaborations; Collection; Computer Models; Computer software; Consult; Data; Data Set; Databases; Decision Trees; Descriptor; Docking; Drug Design; Drug Industry; Drug Interactions; Drug Modelings; Evaluation; Family; Fee-for-Service Plans; Fingerprint; Foundations; Graph; Hela Cells; Hepatocyte; Human; In Vitro; Industry; International; Intuition; Investments; Learning; Libraries; Licensing; Ligands; Literature; Machine Learning; Mediating; Methods; Modeling; Molecular; Natural Products; Nucleoside Transporter; Online Systems; Organ; Output; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Process; Property; PubChem; Public Domains; Pythons; Receiver Operating Characteristics; Reporting; Resources; Risk; Seminal fluid; Site; Software Tools; Structure; Structure-Activity Relationship; System; Testing; Toxic Environmental Substances; Toxic effect; Training; Trees; Universities; Uridine; Validation; Vendor; Virus; Visualization; Work; Xenobiotics; base; clinically relevant; computerized tools; consumer product; data curation; deep learning; design; drug candidate; drug discovery; drug disposition; high throughput screening; improved; in vitro testing; in vivo; inhibitor; inhibitor therapy; interest; long short term memory; machine learning algorithm; machine learning method; machine learning model; member; model building; molecular shape; molnupiravir; neural network; novel therapeutics; pharmacophore; predictive modeling; prospective; prototype; random forest; remdesivir; side effect; software development; tool; tool development; uptake; web app

Summary Being able to predict interactions with important human transporters would be of value to new drug design to avoid compounds that interact with them and cause undesirable side effects. Conversely, some drug transporters can be used for targeting molecules to specific organs and this may have considerable utility. Understanding the interactions of novel drugs, natural products and environmental toxicants and their interactions with an array of such transporters is, therefore, important for several industries, as well as from a regulatory perspective (e.g. FDA, EPA and EMA). Being able to predict such interactions in a fast and reliable manner effectively requires using computational approaches and learning from in vitro data, the latter a resource that is rapidly growing. Over the past 20 years, we have been at the forefront of applying different machine learning approaches to modeling drug transporters and, in many cases, developing datasets for transporters for which there was scant available data. We now propose doing this for several transporters that may be important for drug discovery. In Phase I we focused on OATP1B1 (SLCO1B1), which is an uptake transporter largely restricted to the sinusoidal aspect of hepatocytes where it mediates transport of a variety of structurally unrelated compounds, including members of several clinically important drug families (incl. statins, sartans and angiotensin converting enzyme (ACE) inhibitors). We tested 476 drugs against one substrate in vitro. We then curated these data and built machine learning models using multiple machine learning methods as well as model evaluation metrics. This enabled us to develop models for integration in a web-based software tool called MegaTrans® that enables the user to input their own compound structures and generate predictions for interactions with transporter/s of interest, as well as visualize the similarity to the training set of each model using several different visualization methods. In addition, during Phase I we also performed preliminary data curation, model building and validation for two equilibrative nucleoside transporters (ENTs), ENT1 and ENT2, that are present at the blood testes barrier (BTB), where they can facilitate drug disposition (e.g. for antivirals, thereby potentially eliminating a sanctuary site for viruses detectable in semen). We generated Bayesian and pharmacophore models and used these to predict numerous compounds that were then tested in vitro against ENTs. We used these ENT models to predict (i) the antivirals used in treating COVID-19, remdesivir and molnupiravir, inhibit ENT activity, and that (ii) remdesivir is an ENT substrate, as well as validating these predictions. In Phase II we plan on building on the foundation of Phase I and propose greatly expanding the ENT1 and ENT2 models through in vitro testing (at the University of Arizona) of >2000 approved drugs, natural products, and environmental toxicants as inhibitors of ENT transport. We will use these data to build and validate machine learning models using several algorithms, at Collaborations Pharmaceuticals, Inc. We will also test these models using external validation with additional molecules from vendor libraries and drug collections that are not in the model. In this process we will also build out the capabilities of MegaTransÒ to use 3D pharmacophore descriptors to incorporate molecular shape features and allow 3D searches. The return on investment of such a commercial tool would be that it could assist in the design and selection of more favorable compounds by avoiding transporters of interest (or, conversely, allow the targeting of specific transporters to increase uptake into organs). It could also identify compounds that are already approved that might present a drug-interaction risk. Predicting such behavior seen in vivo is ideal and will lead to the prioritization of compounds to test in vitro for potential drug-drug interactions. In summary, we propose generating large training sets for ENT1 and ENT2 transporters that we will use to generate an array of validated machine learning models of interest to drug discovery (with specific interest for those generating antivirals). MegaTransÒ will be a commercial product available for licensing by pharmaceutical, consumer product, agrochemical and regulatory groups, as well as fee-for-service consulting provided by Collaborations Pharmaceuticals, Inc.

概括 能够预测与重要的人类转运蛋白的相互作用对于新药设计具有重要价值 避免与它们相互作用并引起不良副作用的化合物。相反，一些药物转运蛋白 可用于将分子靶向特定器官，这可能具有相当大的实用性。了解 新药、天然产物和环境毒物的相互作用及其与一系列物质的相互作用 因此，从监管角度来看，此类运输工具对于多个行业都很重要（例如： FDA、EPA 和 EMA）。能够以快速可靠的方式有效地预测此类相互作用需要 使用计算方法并从体外数据中学习，后者是一种正在快速增长的资源。 在过去的 20 年里，我们一直处于应用不同机器学习方法的最前沿 对药物转运蛋白进行建模，并在许多情况下为缺乏药物转运蛋白的转运蛋白开发数据集 可用数据。我们现在建议对几种可能对药物发现很重要的转运蛋白进行此操作。在 第一阶段我们重点关注 OATP1B1 (SLCO1B1)，它是一种主要局限于正弦曲线的摄取转运蛋白 肝细胞的一个方面，它介导多种结构上不相关的化合物的运输，包括 几个临床重要药物家族的成员（包括他汀类药物、沙坦类药物和血管紧张素转换酶） （ACE）抑制剂）。我们在体外针对一种底物测试了 476 种药物。然后我们整理这些数据并构建 使用多种机器学习方法以及模型评估指标的机器学习模型。这 使我们能够开发模型以集成到名为 MegaTrans® 的基于网络的软件工具中，该工具使 用户输入自己的化合物结构并生成与转运蛋白相互作用的预测 兴趣，以及使用几种不同的可视化来可视化每个模型与训练集的相似性 方法。此外，在第一阶段我们还进行了初步的数据整理、模型构建和验证 对于存在于血睾屏障处的两种平衡核苷转运蛋白 (ENT) ENT1 和 ENT2 （BTB），它们可以促进药物处置（例如抗病毒药物，从而有可能消除避难所 精液中可检测到病毒的位点）。我们生成了贝叶斯模型和药效团模型，并将其用于 预测了许多化合物，然后在体外针对耳鼻喉科进行了测试。我们使用这些耳鼻喉科模型来预测 (i) 用于治疗 COVID-19 的抗病毒药物瑞德西韦和莫努匹拉韦可抑制耳鼻喉科活性，并且 (ii) 瑞德西韦是一种耳鼻喉底物，也验证了这些预测。在第二阶段，我们计划在 第一阶段的基础，并建议通过体外测试大大扩展 ENT1 和 ENT2 模型（在 亚利桑那大学）超过 2000 种批准药物、天然产物和环境毒物作为抑制剂 耳鼻喉科运输。我们将使用这些数据来使用多种算法构建和验证机器学习模型， 在 Collaborations Pharmaceuticals, Inc. 我们还将使用外部验证和附加功能来测试这些模型 来自模型中未包含的供应商库和药物集合的分子。在此过程中我们还将构建 展示 MegaTransÒ 使用 3D 药效团描述符整合分子形状的能力 功能并允许 3D 搜索。这种商业工具的投资回报将是它可以帮助 通过避免感兴趣的转运蛋白来设计和选择更有利的化合物（或者相反， 允许靶向特定转运蛋白以增加器官的摄取）。它还可以识别以下化合物： 已被批准可能存在药物相互作用风险的药物。预测体内的这种行为是理想的 并将导致化合物的优先顺序在体外测试潜在的药物相互作用。总之， 我们建议为 ENT1 和 ENT2 转运蛋白生成大型训练集，用于生成数组 对药物发现感兴趣的经过验证的机器学习模型（特别感兴趣的是那些生成 抗病毒药）。 MegaTransÒ 将成为可供制药、消费者许可的商业产品 产品、农用化学品和监管小组，以及 Collaborations 提供的收费服务咨询 制药公司